A Dual Closed-Loop Digital Twin Construction Method for Optimizing the Copper Disc Casting Process

The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.

Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing rapidly,the competition is becoming increasingly fierce,and the digital transformation of the production line is imminent.As one of themost important components of heavy vehicles,the transmission front andmiddle case assembly lines have a high degree of automation,which can be used as a pilot for the digital transformation of production.To ensure the visualization of digital twins(DT),consistent control logic,and real-time data interaction,this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line.Firstly,theDT-based systemarchitecture is designed,and theDT model is created by constructing the visualization model,logic model,and data model of the assembly line.Then,a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line.Eventually,some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment.

Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.

Data-Centric Approach to Digital Twin Modeling of Production Lines

Digital twin(DT) is a virtual replica of a physical world that has become one of the most important ideas in the manufacturing industry’s digital revolution. DT modeling is a vital issue in building a DT of a production line. In this paper, a method is proposed to address the difficulties of complicated production line business and data heterogeneity. The method focuses on essential data in the production line and creates conceptual and information models based on the ArtiFlow model and AutomationML(AML). Conceptual models are mainly used to describe and analyze the business activities of the production line, and information models describe real production lines in the form of XML files. The proposed modeling approach has been applied to a real-world clothing production line to demonstrate its feasibility and effectiveness.

微电网孤岛运行工况下DT模型自适应迁移方法

微电网在孤岛运行工况变化情况下,会造成数字孪生(DT)模型迁移时无法准确匹配源域模型,从而导致迁移效率偏低等问题。为此,研究提出微电网孤岛运行工况变化场景下的数字孪生模型自适应迁移方法。通过分析微电网在孤岛运行工况下的运行特点,建立数字孪生模型,利用微电网孤岛运行工况的时变性,匹配计算源域模型,并通过缩小不同微电网孤岛运行工况下的源域数据分布差异,实现数字孪生模型的自适应迁移。实验结果表明,电流负载、实际电流等部分特征数据迁移效果较好,且具有较高迁移效率,验证了所提迁移方法可以适应不同微电网孤岛运行工况,具有较好的实用性。

DTS技术及其展望

为提高调度员对电力系统正常调度控制以及处理各种突发事件的应变能力 ,调度员培训仿真技术(DTS)在电力系统中得到了广泛的应用。文中对DTS技术的要求、分类以及结构作了详细的介绍。同时通过对DTS发展现状的分析 。

DTS、杜比多声道环绕声技术的发展与应用

随着多声道环绕声技术在电影院、家庭影院、高清电视、环绕声广播、汽车产品以及相关电子产品中得到应用和普及,为了使听众得到更好的环绕声体验,杜比实验室与DTS公司不断对环绕声技术进行研究与探索,针对这两家公司在目前市场上客户应用范围较广、用户使用较多的5.1,6.1,7.1及n.1声道环绕声技术的发展与应用进行了对比与分析。

数字电影院系统DTS技术及应用

DTS(数字电影院系统)是一种电影数字多声道系统,是家庭环绕声技术中出现的一项全新技术,阐述了DTS数字电影院系统的原理、特点,介绍了DTS数字电影院系统所采用的最新技术.

DTS音频文件软件解码的实现

介绍了DTS音频文件的解码标准,给出了软件解码需要的开发环境和解码流程,对3个DTS音频文件的软件解码结果进行了分析,最后列举了在编写DTS音频文件解码软件过程中需要注意的一些问题。

Digital Twins and Cyber Physical Systems toward Smart Manufacturing and Industry 4.0:Correlation and Comparison

State-of-the-art technologies such as the Internet of Things(IoT),cloud computing(CC),big data analytics(BDA),and artificial intelligence(AI)have greatly stimulated the development of smart manufacturing.An important prerequisite for smart manufacturing is cyber-physical integration,which is increasingly being embraced by manufacturers.As the preferred means of such integration,cyber-physical systems(CPS)and digital twins(DTs)have gained extensive attention from researchers and practitioners in industry.With feedback loops in which physical processes affect cyber parts and vice versa,CPS and DTs can endow manufacturing systems with greater efficiency,resilience,and intelligence.CPS and DTs share the same essential concepts of an intensive cyber-physical connection,real-time interaction,organization integration,and in-depth collaboration.However,CPS and DTs are not identical from many perspectives,including their origin,development,engineering practices,cyber-physical mapping,and core elements.In order to highlight the differences and correlation between them,this paper reviews and analyzes CPS and DTs from multiple perspectives.

Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis

This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training.

Modeling and Analysis of Production Logistics Spatio-Temporal Graph Network Driven by Digital Twin

In the process of logistics distribution of manufacturing enterprises, the automatic scheduling method based on the algorithm model has the advantages of accurate calculation and stable operation, but it excessively relies on the results of data calculation, ignores historical information and empirical data in the solving process, and has the bottleneck of low processing dimension and small processing scale. Therefore, in the digital twin(DT) system based on virtual and real fusion, a modeling and analysis method of production logistics spatio-temporal graph network model is proposed, considering the characteristics of road network topology and time-varying data. In the DT system, the temporal graph network model of the production logistics task is established and combined with the network topology, and the historical scheduling information about logistics elements is stored in the nodes. When the dynamic task arrives, a multi-stage links probability prediction method is adopted to predict the possibility of loading, driving, and other link relationships between task-related entity nodes at each stage. Several experiments are carried out, and the prediction accuracy of the digital twin-based temporal graph network(DTGN) model trained by historical scheduling information reaches 99.2% when the appropriate batch size is selected. Through logistics simulation experiments, the feasibility and the effectiveness of production logistics spatio-temporal graph network analysis methods based on historical scheduling information are verified.

基于数字孪生的多自动驾驶车辆分布式协同路径规划算法

针对多辆自动驾驶车辆(AVs)在进行路径规划过程中存在的车辆之间协作难、协作训练出来的模型质量低以及所求结果直接应用到物理车辆的效果较差的问题,该文提出一种基于数字孪生(DT)的多AVs分布式协同路径规划算法,基于可信度加权去中心化的联邦强化学习方法(CWDFRL)来实现多AVs的路径规划。首先将单个AVs的路径规划问题建模成在驾驶行为约束下的最小化平均任务完成时间问题,并将其转化成马尔可夫决策过程(MDP),使用深度确定性策略梯度算法(DDPG)进行求解;然后使用联邦学习(FL)保证车辆之间的协同合作,针对集中式的FL中存在的全局模型更新质量低的问题,使用基于可信度的动态节点选择的去中心化FL训练方法改善了全局模型聚合质量低的问题;最后使用DT辅助去中心化联邦强化学习(DFRL)模型的训练,利用孪生体可以从DT环境中学习的优点,快速将训练好的模型直接部署到现实世界的AVs上。仿真结果表明,与现有的方法相比,所提训练框架可以得到一个较高的奖励,有效地提高了车辆对其本身速度的利用率,与此同时还降低了车辆群体的平均任务完成时间和碰撞概率。

基于智能分层切片技术的数字孪生传感信息同步策略

针对传感数据在无线接入网(RAN)中传输的不可靠性与不及时性造成数字孪生(DTs)同步信息的不精确问题,该文提出一种基于智能分层切片技术的DTs传感信息同步策略。该策略在双时间尺度下,以最大化传感信息满意度和最小化切片重配置及DTs同步成本为目标,联合优化切片无线资源配置以及DTs传感信息同步问题。首先,在大时间尺度,利用网络切片为有着不同服务质量(QoS)的DTs提供隔离以及解决部署问题;在小时间尺度,通过更加灵活的无线资源分配来提高DTs传感信息同步任务对动态环境的适应性,进一步提高通信性能,建立更逼近于物理实体的DTs。其次,为了求解不同时间尺度的优化问题,该文提出一种双层深度强化学习(DRL)框架实现高效的网络资源交互,其中下层控制算法利用优先经验放回(PER)机制加快收敛速度。最后,仿真结果验证了所提策略的有效性。

工业物联网中数字孪生辅助任务卸载算法

针对工业物联网(IIoT)设备资源有限和边缘服务器资源动态变化导致的任务协同计算效率低等问题,该文提出一种工业物联网中数字孪生(DT)辅助任务卸载算法。首先,该算法构建了云-边-端3层数字孪生辅助任务卸载框架,在所创建的数字孪生层中生成近似最佳的任务卸载策略。其次,在任务计算时间和能量的约束下,从时延的角度研究了计算卸载过程中用户关联和任务划分的联合优化问题,建立了最小化任务卸载时间和服务失败惩罚的优化模型。最后,提出一种基于深度多智能体参数化Q网络(DMAPQN)的用户关联和任务划分算法,通过每个智能体不断地探索和学习,以获取近似最佳的用户关联和任务划分策略,并将该策略下发至物理实体网络中执行。仿真结果表明,所提任务卸载算法有效降低了任务协同计算时间,同时为每个计算任务提供近似最佳的卸载策略。

基于数字孪生的加工生产线虚实交互技术研究

针对传统加工生产线运行过程中设备数据采集困难和可视化程度低的问题,研究了数字孪生(DT)技术在加工生产线虚实交互方面的应用问题。首先,以某电机外壳加工生产线为例,分析了生产线的加工流程,设计了虚实交互技术的总体框架,并提出了具体的方法;然后,对加工生产线进行了数字孪生体建模,对模型中的关键要素,几何、物理、行为属性及通讯接口的构建进行了详细的阐述;最后,采用过程控制的对象链接和嵌入(OPC)技术进行了加工生产线中多源异构数据的采集与处理,完成了实时数据间的映射。研究结果表明:采用虚实交互技术可以完成物理生产线与数字孪生体间的实时映射;电机外壳加工生产线中机械臂虚实交互的可靠性为99.95%,可满足精确性要求;采用服务模块能够更加直观、有效地反映生产线的实际加工状态,可满足加工过程信息动态可视化要求,证明了该方案具有可行性和有效性。

基于DT-CWT的数字水印算法

为了实现数字图像的版权保护,设计了一种基于双树复小波变换的抗几何攻击的数字水印算法:对水印图像进行三级双树复小波变换,提取特征并插值为特征矩阵,并且定义了一种新的距离向量用于相似性度量,使用特征矩阵对遭受几何攻击的图像进行参数估计。实验结果表明,该算法可用于对图像遭受的对称缩放、非对称缩放、旋转等几何攻击进行校正,确保水印嵌入与检测的同步。

A digital twin-enhanced collaborative maintenance paradigm for aero-engine fleet

Maintenance of aero-engine fleets is crucial for the efficiency,safety,and reliability of the aviation industry.With the increasing demand for air transportation,maintaining high-performing aero-engines has become significant.Collaborative maintenance,specifically targeting aero-engine fleets,involves the coordination of multiple tasks and resources to enhance management efficiency and reduce costs.Digital Twin(DT)technology provides essential technical support for the intelligent operation and maintenance of aero-engine fleets.DT maps physical object properties to the virtual world,creating high-fidelity,dynamic models.However,DT-enhanced collaborative maintenance faces various challenges,including the construction of complex system-layer DT models,management of massive integrated DT data,and the development of fusion mechanisms and decision-making methods for DT data and models.Overcoming these challenges will allow the aviation industry to optimize aero-engine fleet maintenance,ensuring safety,efficiency,and cost-effectiveness while meeting the growing demand for air transportation.